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초록

Understanding what makes online reviews helpful remains a central concern in information systems research and platform design. While prior studies have examined sentiment and structure, less is known about how distinct linguistic tones—analytical, authentic, and mixed emotional—signal reviewer credibility in text-only environments. Drawing on Source Credibility Theory (SCT) and Cognitive Fit Theory (CFT), we propose that these tones act as stylistic heuristics for perceived expertise and trustworthiness, particularly when identity cues are absent and tone aligns with the reader’s evaluative goals. Using LIWC to analyze 54,759 Amazon reviews across search and experience goods, we find that all three tones enhance perceived helpfulness, but their effects vary by product type. Analytical tone is more persuasive for search goods, where structured reasoning signals expertise and supports attribute-based comparisons. Authentic tone is more effective for experience goods, where sincerity supports trust and affective simulation. We also introduce mixed emotional tone—the co-expression of positive and negative affect—as a novel signal of deliberation, which improves helpfulness ratings, especially for search goods. These findings clarify prior inconsistencies in sentiment research and advance a theory-driven framework for how tone-product fit influences review helpfulness in digital platforms.

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